from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI) benchmarks: perfect hyperparameters match

Reporting(config="config.yml").run()

KNeighborsClassifier: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=brute.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.173	0.0	4.627	0.0	1	100	NaN	NaN	0.576	0.0	0.300	0.0	See	See
3	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.148	0.0	5.391	0.0	1	5	NaN	NaN	0.563	0.0	0.263	0.0	See	See
6	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.140	0.0	5.704	0.0	1	1	NaN	NaN	0.561	0.0	0.250	0.0	See	See
9	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.145	0.0	5.503	0.0	-1	100	NaN	NaN	0.546	0.0	0.266	0.0	See	See
12	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.134	0.0	5.957	0.0	-1	5	NaN	NaN	0.559	0.0	0.240	0.0	See	See
15	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.153	0.0	5.216	0.0	-1	1	NaN	NaN	0.563	0.0	0.272	0.0	See	See
18	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.062	0.0	0.258	0.0	1	100	NaN	NaN	0.099	0.0	0.629	0.0	See	See
21	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.059	0.0	0.273	0.0	1	5	NaN	NaN	0.102	0.0	0.576	0.0	See	See
24	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.055	0.0	0.289	0.0	1	1	NaN	NaN	0.102	0.0	0.544	0.0	See	See
27	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.073	0.0	0.220	0.0	-1	100	NaN	NaN	0.107	0.0	0.679	0.0	See	See
30	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.064	0.0	0.251	0.0	-1	5	NaN	NaN	0.106	0.0	0.604	0.0	See	See
33	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.063	0.0	0.254	0.0	-1	1	NaN	NaN	0.113	0.0	0.560	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier	predict	1000000	1000	100	NaN	27.161	0.347	0.0	0.027	1	100	0.929	0.818	4.567	0.054	5.947	0.103	See	See
2	KNeighborsClassifier	predict	1000000	1	100	NaN	0.242	0.006	0.0	0.242	1	100	1.000	0.000	0.110	0.003	2.206	0.078	See	See
4	KNeighborsClassifier	predict	1000000	1000	100	NaN	27.250	0.212	0.0	0.027	1	5	0.829	0.818	4.554	0.027	5.984	0.058	See	See
5	KNeighborsClassifier	predict	1000000	1	100	NaN	0.236	0.005	0.0	0.236	1	5	1.000	0.000	0.113	0.003	2.086	0.075	See	See
7	KNeighborsClassifier	predict	1000000	1000	100	NaN	18.240	0.153	0.0	0.018	1	1	0.720	0.696	4.553	0.018	4.006	0.037	See	See
8	KNeighborsClassifier	predict	1000000	1	100	NaN	0.234	0.007	0.0	0.234	1	1	1.000	0.000	0.108	0.004	2.160	0.098	See	See
10	KNeighborsClassifier	predict	1000000	1000	100	NaN	41.247	0.000	0.0	0.041	-1	100	0.929	0.696	4.556	0.029	9.053	0.058	See	See
11	KNeighborsClassifier	predict	1000000	1	100	NaN	0.225	0.020	0.0	0.225	-1	100	1.000	0.000	0.112	0.005	2.014	0.203	See	See
13	KNeighborsClassifier	predict	1000000	1000	100	NaN	41.592	0.000	0.0	0.042	-1	5	0.829	0.917	4.664	0.042	8.919	0.080	See	See
14	KNeighborsClassifier	predict	1000000	1	100	NaN	0.224	0.016	0.0	0.224	-1	5	1.000	1.000	0.112	0.002	2.000	0.149	See	See
16	KNeighborsClassifier	predict	1000000	1000	100	NaN	32.929	0.000	0.0	0.033	-1	1	0.720	0.917	4.664	0.015	7.061	0.023	See	See
17	KNeighborsClassifier	predict	1000000	1	100	NaN	0.219	0.024	0.0	0.219	-1	1	1.000	1.000	0.108	0.004	2.028	0.232	See	See
19	KNeighborsClassifier	predict	1000000	1000	2	NaN	20.472	0.283	0.0	0.020	1	100	0.983	0.984	1.015	0.007	20.173	0.314	See	See
20	KNeighborsClassifier	predict	1000000	1	2	NaN	0.026	0.001	0.0	0.026	1	100	1.000	1.000	0.005	0.000	5.585	0.377	See	See
22	KNeighborsClassifier	predict	1000000	1000	2	NaN	20.485	0.182	0.0	0.020	1	5	0.982	0.984	1.017	0.014	20.150	0.333	See	See
23	KNeighborsClassifier	predict	1000000	1	2	NaN	0.025	0.001	0.0	0.025	1	5	1.000	1.000	0.005	0.000	5.167	0.400	See	See
25	KNeighborsClassifier	predict	1000000	1000	2	NaN	12.329	0.057	0.0	0.012	1	1	0.976	0.973	1.025	0.027	12.022	0.316	See	See
26	KNeighborsClassifier	predict	1000000	1	2	NaN	0.017	0.001	0.0	0.017	1	1	1.000	1.000	0.005	0.000	3.447	0.384	See	See
28	KNeighborsClassifier	predict	1000000	1000	2	NaN	34.726	0.000	0.0	0.035	-1	100	0.983	0.973	1.017	0.015	34.136	0.516	See	See
29	KNeighborsClassifier	predict	1000000	1	2	NaN	0.035	0.004	0.0	0.035	-1	100	1.000	1.000	0.005	0.000	6.765	0.815	See	See
31	KNeighborsClassifier	predict	1000000	1000	2	NaN	34.899	0.000	0.0	0.035	-1	5	0.982	0.987	1.104	0.015	31.598	0.428	See	See
32	KNeighborsClassifier	predict	1000000	1	2	NaN	0.036	0.005	0.0	0.036	-1	5	1.000	1.000	0.005	0.001	7.063	1.148	See	See
34	KNeighborsClassifier	predict	1000000	1000	2	NaN	26.616	0.188	0.0	0.027	-1	1	0.976	0.987	1.111	0.026	23.960	0.577	See	See
35	KNeighborsClassifier	predict	1000000	1	2	NaN	0.024	0.002	0.0	0.024	-1	1	1.000	1.000	0.005	0.000	4.743	0.407	See	See

KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=kd_tree.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.778	0.0	0.029	0.0	-1	5	NaN	NaN	0.816	0.0	3.404	0.0	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.882	0.0	0.028	0.0	-1	100	NaN	NaN	0.799	0.0	3.609	0.0	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.004	0.0	0.027	0.0	1	5	NaN	NaN	0.785	0.0	3.826	0.0	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.211	0.0	0.025	0.0	1	100	NaN	NaN	0.771	0.0	4.164	0.0	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.189	0.0	0.025	0.0	1	1	NaN	NaN	0.780	0.0	4.089	0.0	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.082	0.0	0.026	0.0	-1	1	NaN	NaN	0.792	0.0	3.889	0.0	See	See
18	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.763	0.0	0.021	0.0	-1	5	NaN	NaN	0.541	0.0	1.410	0.0	See	See
21	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.774	0.0	0.021	0.0	-1	100	NaN	NaN	0.550	0.0	1.406	0.0	See	See
24	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.699	0.0	0.023	0.0	1	5	NaN	NaN	0.543	0.0	1.286	0.0	See	See
27	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.740	0.0	0.022	0.0	1	100	NaN	NaN	0.527	0.0	1.404	0.0	See	See
30	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.803	0.0	0.020	0.0	1	1	NaN	NaN	0.538	0.0	1.493	0.0	See	See
33	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.763	0.0	0.021	0.0	-1	1	NaN	NaN	0.513	0.0	1.487	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.898	0.025	0.000	0.001	-1	5	0.971	0.970	0.710	0.015	1.265	0.045	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.004	0.001	0.000	0.004	-1	5	1.000	1.000	0.001	0.000	4.371	1.737	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	3.043	0.031	0.000	0.003	-1	100	0.969	0.970	0.704	0.018	4.323	0.119	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.006	0.001	0.000	0.006	-1	100	1.000	1.000	0.001	0.000	6.781	2.624	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	1.589	0.028	0.000	0.002	1	5	0.971	0.971	0.227	0.005	6.983	0.192	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	4.469	2.491	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	5.170	0.163	0.000	0.005	1	100	0.969	0.971	0.233	0.016	22.218	1.695	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.001	0.000	0.003	1	100	1.000	1.000	0.000	0.000	8.391	4.089	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.830	0.023	0.000	0.001	1	1	0.951	0.958	0.129	0.004	6.456	0.268	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	4.336	2.242	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.438	0.010	0.000	0.000	-1	1	0.951	0.958	0.130	0.004	3.366	0.125	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.001	0.000	0.003	-1	1	1.000	1.000	0.000	0.000	11.840	6.816	See	See
19	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.032	0.002	0.000	0.000	-1	5	0.980	0.988	0.008	0.001	3.920	0.444	See	See
20	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.001	0.000	0.003	-1	5	1.000	1.000	0.000	0.000	16.135	10.317	See	See
22	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.048	0.003	0.000	0.000	-1	100	0.983	0.988	0.008	0.001	5.944	0.610	See	See
23	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.001	0.000	0.003	-1	100	1.000	1.000	0.000	0.000	15.214	8.485	See	See
25	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.029	0.002	0.001	0.000	1	5	0.980	0.984	0.001	0.000	21.826	5.932	See	See
26	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	4.108	2.204	See	See
28	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.060	0.001	0.000	0.000	1	100	0.983	0.984	0.001	0.000	45.798	12.211	See	See
29	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	100	1.000	1.000	0.000	0.000	3.066	2.962	See	See
31	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.028	0.001	0.001	0.000	1	1	0.977	0.979	0.001	0.000	31.431	8.334	See	See
32	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	4.598	2.788	See	See
34	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.029	0.001	0.001	0.000	-1	1	0.977	0.979	0.001	0.000	31.800	10.049	See	See
35	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.000	0.000	0.003	-1	1	1.000	1.000	0.000	0.000	16.588	8.633	See	See

KMeans_tall: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	30	0.747	0.0	0.643	0.0	k-means++	NaN	30	NaN	0.336	0.0	2.226	0.0	See	See
3	KMeans_tall	fit	1000000	1000000	2	30	0.604	0.0	0.794	0.0	random	NaN	30	NaN	0.384	0.0	1.572	0.0	See	See
6	KMeans_tall	fit	1000000	1000000	100	30	9.206	0.0	2.607	0.0	k-means++	NaN	30	NaN	4.243	0.0	2.170	0.0	See	See
9	KMeans_tall	fit	1000000	1000000	100	30	8.102	0.0	2.962	0.0	random	NaN	30	NaN	4.477	0.0	1.810	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	30	0.002	0.000	0.260	0.000	k-means++	0.000	30	0.000	0.0	0.0	7.773	3.758	See	See
2	KMeans_tall	predict	1000000	1	2	30	0.002	0.000	0.000	0.002	k-means++	1.000	30	1.000	0.0	0.0	10.351	4.568	See	See
4	KMeans_tall	predict	1000000	1000	2	30	0.002	0.000	0.266	0.000	random	0.000	30	0.001	0.0	0.0	7.822	3.800	See	See
5	KMeans_tall	predict	1000000	1	2	30	0.002	0.000	0.000	0.002	random	1.000	30	1.000	0.0	0.0	9.323	5.322	See	See
7	KMeans_tall	predict	1000000	1000	100	30	0.002	0.001	10.660	0.000	k-means++	0.001	30	0.002	0.0	0.0	6.792	3.341	See	See
8	KMeans_tall	predict	1000000	1	100	30	0.002	0.000	0.013	0.002	k-means++	1.000	30	1.000	0.0	0.0	8.529	5.850	See	See
10	KMeans_tall	predict	1000000	1000	100	30	0.002	0.000	10.986	0.000	random	0.002	30	0.001	0.0	0.0	6.860	2.831	See	See
11	KMeans_tall	predict	1000000	1	100	30	0.002	0.000	0.014	0.002	random	1.000	30	1.000	0.0	0.0	7.870	4.558	See	See

KMeans_short: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	20	0.334	0.0	0.010	0.0	k-means++	NaN	20	NaN	0.055	0.0	6.047	0.0	See	See
3	KMeans_short	fit	10000	10000	2	20	0.106	0.0	0.030	0.0	random	NaN	20	NaN	0.144	0.0	0.733	0.0	See	See
6	KMeans_short	fit	10000	10000	100	20	1.202	0.0	0.133	0.0	k-means++	NaN	20	NaN	0.281	0.0	4.273	0.0	See	See
9	KMeans_short	fit	10000	10000	100	20	0.360	0.0	0.444	0.0	random	NaN	20	NaN	0.643	0.0	0.560	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.142	0.000	k-means++	0.002	20	0.002	0.001	0.0	2.994	0.445	See	See
2	KMeans_short	predict	10000	1	2	20	0.002	0.0	0.000	0.002	k-means++	1.000	20	1.000	0.000	0.0	8.272	3.770	See	See
4	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.138	0.000	random	0.001	20	0.003	0.001	0.0	2.887	0.565	See	See
5	KMeans_short	predict	10000	1	2	20	0.002	0.0	0.000	0.002	random	1.000	20	1.000	0.000	0.0	9.768	4.044	See	See
7	KMeans_short	predict	10000	1000	100	20	0.003	0.0	4.647	0.000	k-means++	0.288	20	0.375	0.002	0.0	1.903	0.355	See	See
8	KMeans_short	predict	10000	1	100	20	0.002	0.0	0.008	0.002	k-means++	1.000	20	1.000	0.000	0.0	8.094	3.898	See	See
10	KMeans_short	predict	10000	1000	100	20	0.004	0.0	4.389	0.000	random	0.322	20	0.301	0.002	0.0	2.196	0.338	See	See
11	KMeans_short	predict	10000	1	100	20	0.002	0.0	0.008	0.002	random	1.000	20	1.000	0.000	0.0	7.780	3.635	See	See

LogisticRegression: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	[20]	17.538	0.0	[-0.06727524]	0.000	NaN	NaN	NaN	NaN	NaN	3.170	0.0	5.533	0.0	See	See
3	LogisticRegression	fit	1000	1000	10000	[26]	1.434	0.0	[1.45037389]	0.001	NaN	NaN	NaN	NaN	NaN	1.417	0.0	1.012	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	[20]	0.000	0.0	[41.59640774]	0.0	NaN	NaN	NaN	NaN	0.541	0.000	0.0	0.872	0.398	See	See
2	LogisticRegression	predict	1000000	1	100	[20]	0.000	0.0	[0.12016568]	0.0	NaN	NaN	NaN	NaN	1.000	0.000	0.0	0.449	0.320	See	See
4	LogisticRegression	predict	1000	100	10000	[26]	0.003	0.0	[79.58656006]	0.0	NaN	NaN	NaN	NaN	0.310	0.004	0.0	0.656	0.094	See	See
5	LogisticRegression	predict	1000	1	10000	[26]	0.000	0.0	[13.64994198]	0.0	NaN	NaN	NaN	NaN	1.000	0.001	0.0	0.160	0.071	See	See

Ridge: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	NaN	0.344	0.0	0.232	0.0	NaN	NaN	NaN	0.341	0.0	1.009	0.0	See	See
3	Ridge	fit	1000000	1000000	100	NaN	1.836	0.0	0.436	0.0	NaN	NaN	NaN	0.429	0.0	4.278	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	NaN	0.012	0.001	6.457	0.0	NaN	NaN	0.115	0.023	0.002	0.551	0.047	See	See
2	Ridge	predict	1000	1	10000	NaN	0.000	0.000	0.630	0.0	NaN	NaN	NaN	0.000	0.000	0.694	0.562	See	See
4	Ridge	predict	1000000	1000	100	NaN	0.000	0.000	3.848	0.0	NaN	NaN	1.000	0.000	0.000	0.622	0.349	See	See
5	Ridge	predict	1000000	1	100	NaN	0.000	0.000	0.007	0.0	NaN	NaN	NaN	0.000	0.000	0.717	0.605	See	See